code: def forward(
      self,
      hidden_states,
      attention_mask=None,
      head_mask=None,
      encoder_hidden_states=None,
      encoder_attention_mask=None,
  ):
      mixed_query_layer = self.query(hidden_states)
  
      # If this is instantiated as a cross-attention module, the keys
      # and values come from an encoder; the attention mask needs to be
      # such that the encoder's padding tokens are not attended to.
      if encoder_hidden_states is not None:
          mixed_key_layer = self.key(encoder_hidden_states)
          mixed_value_layer = self.value(encoder_hidden_states)
          attention_mask = encoder_attention_mask
      else:
          mixed_key_layer = self.key(hidden_states)
          mixed_value_layer = self.value(hidden_states)
  
      query_layer = self.transpose_for_scores(mixed_query_layer)
      key_layer = self.transpose_for_scores(mixed_key_layer)
      value_layer = self.transpose_for_scores(mixed_value_layer)
  
      # Take the dot product between "query" and "key" to get the raw attention scores.
      # Quantized matrix multiplication. Achieved by quantizing the inputs to torch.matmul().
      attention_scores = torch.matmul(
          self.matmul_q_input_quantizer(query_layer),
          self.matmul_k_input_quantizer(key_layer.transpose(-1, -2)),
      )
      attention_scores = attention_scores / math.sqrt(
          self.attention_head_size
      )
      if attention_mask is not None:
          # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
          attention_scores = attention_scores + attention_mask
  
      # Normalize the attention scores to probabilities.
      attention_probs = nn.Softmax(dim=-1)(attention_scores)
  
      # This is actually dropping out entire tokens to attend to, which might
      # seem a bit unusual, but is taken from the original Transformer paper.
      attention_probs = self.dropout(attention_probs)
  
      # Mask heads if we want to
      if head_mask is not None:
          attention_probs = attention_probs * head_mask
  
      # Quantized matrix multiplication. Achieved by quantizing the inputs to torch.matmul().
      context_layer = torch.matmul(
          self.matmul_a_input_quantizer(attention_probs),
          self.matmul_v_input_quantizer(value_layer),
      )
  
      context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
      new_context_layer_shape = context_layer.size()[:-2] + (
          self.all_head_size,
      )
      context_layer = context_layer.view(*new_context_layer_shape)
  
      outputs = (
          (context_layer, attention_probs)
          if self.output_attentions
          else (context_layer,)
      )
      return outputs
  
file_path: lightseq/lightseq/training/pytorch_quantization/nn/modules/quant_bert.py
imports: import math
  import torch
  from torch import nn
  from lightseq.training.pytorch_quantization import nn
  from lightseq.training.pytorch_quantization.nn.modules.tensor_quantizer import TensorQuantizer
label_desc: Use image node_id 3 for calling the QuantBertSelfAttention obj's underlying member method code with example usage: obj.forward(hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask) and returns: outputs